On the optimization of free resources using non-homogeneous Markov chain software rejuvenation model

Koutras, Vasilis P.; Platis, Agapios Ν.; Gravvanis, George A.

doi:10.1016/j.ress.2006.09.017

Cited by 24 publications

(6 citation statements)

References 9 publications

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“…Algorithms including Markov models can solve multi-objective optimization problems [63] with non-homogeneous state space [64,65] in a dynamic environment [66]. Simulation-based optimization algorithms for Markov-chains help to maximize the average reward of a parameterized Markov-chain [67,68].…”

Section: Markov-chain Modellingmentioning

confidence: 99%

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

2018

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Manufacturing and service processes are composed of several elements: Technical, financial, logistics, information and human resources. Staff deployment and staffing is an essential problem in the human resource management domain because the structure of employees would be continuously in an optimal relationship to the jobs to be performed. This paper proposes a conceptual model for the analysis of human resource deployment processes. After a systematic literature review, it was found that algorithms are important tools for the design and control of human resource problems since a wide range of models determines an optimization problem. According to that, the main focus of this research is the modelling and analysis of human resource deployment processes of manufacturing companies using Markov-chain mathematics, also taking into account the absorbing phenomena of employees’ promotion. The main contribution of this article includes the model framework of Markov-chain simulation of a human resource deployment problem; the mathematical description of different human resource deployment strategies with subdiagonal and superdiagonal promotion matrices; the computational results of the described model with different datasets and scenarios. In the case of a given human resource strategy, the Markovian human resource deployment process of a company was analyzed. The analyzed model was the HR deployment of assembly line operators in a multinational company, including six levels of promotion. The results of the scenario analysis show that promotion and recruitment rates have a great impact on the future employees’ structure.

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Section: Markov-chain Modellingmentioning

confidence: 99%

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

2018

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“…A number of studies have looked at resource failures in distributed environments [28,8,29,30,31,32,33,34,35]. Schroeder and Gibson [28] analyse failure data collected over 9 years at Los Alamos National Laboratory (LANL), and includes 23,000 failures recorded on more than 20 different systemsmostly large clusters of Symmetric-Multi-Processing (SMP) and Non-Uniform-Memory-Access (NUMA) nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Koutras, Platis Gravvanis [33] explored the use of homogeneous continuous time Markov chain with the amount of free memory to model the resource degradation of a computer system. Furthermore, the use of a cyclic non-homogeneous continuous time Markov chain in terms of driving an optimal software rejuvenation model is studied [34]. Dai, Levitin and Wang investigate maximizing the expected profit in Grid systems by partitioning the service task into subtasks and by distributing them among the available resources [36].…”

Section: Related Workmentioning

confidence: 99%

Resource failures risk assessment modelling in distributed environments

Alsoghayer

Djemame

2014

Journal of Systems and Software

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Service providers offer access to resources and services in distributed environments such as Grids and Clouds through formal Service level Agreements (SLA), and need well-balanced infrastructures so that they can maximise the Quality of Service (QoS) they offer and minimise the number of SLA violations. We propose a mathematical model to predict the risk of failure of resources in such environments using a discrete-time analytical model driven by reliability functions fitted to observed data. The model relies on the resource historical data so as to predict the risk of failure for a given time interval. The model is evaluated by comparing the predicted risk of failure with the observed risk of failure, and is shown to accurately predict the resources risk of failure, allowing a service provider to selectively choose which SLA request to accept.

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“…Software aging has been detected in numerous types of systems, such as critical systems, telecommunication switching, and billing software programs [2], Android smartphones [3], virtual machine [4], networked UNIX workstations [5], Openstack cloud computing platform [6], Apache web server [7], spacecraft flight systems [8], and real-time systems [9]. Also, software aging could cause great losses in the safety-critical systems, including the loss of human lives [10].…”

Section: Introductionmentioning

confidence: 99%

A software availability model based on multilevel software rejuvenation and markov chain

Ghobadi¹,

Rashidi²

2021

Turk J Elec Eng & Comp Sci

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Increasing use of software, rapid and unavoidable changes in the operational environment bring many problems for software engineers. One of these problems is the aging and degradation of software performance. Software rejuvenation is a proactive and preventive approach to counteract software aging. Generally, when software is initiated, amounts of memory are allocated. Then, the body of software is executed for providing a service and when the software is terminated, the allocated memory is released. In this paper, a rejuvenation model based on multilevel software rejuvenation and Markov chain presented. In this model, the system performance as a result of degraded physical memory and memory usage is divided into four equal levels by services. Hence, we offer four types of policies for software rejuvenation. In addition, the system availability is determined, and a cost function for the model is introduced. The cost function includes the time of performing rejuvenation, the number of system services at any time, and the number of rejuvenation actions. To validate the proposed model, a case study in the banking system in Iran has been studied. Due to the differences in the use of the system over time, it is better to perform the four different policies with regard to the use of the system. The numerical results show that the proposed model is convenient for the system so that the costs are reduced per day.

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On the optimization of free resources using non-homogeneous Markov chain software rejuvenation model

Cited by 24 publications

References 9 publications

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

Resource failures risk assessment modelling in distributed environments

A software availability model based on multilevel software rejuvenation and markov chain

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